{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Les modélisations\n", "\n", "Code sous licence creative commun CC BY-NC-SA BY Gaëlle Rebolini" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ":download:`Télécharger le pdf <./11-modelisation.pdf>`\n", "\n", ":download:`Télécharger le notebook <./11-modelisation.ipynb>`\n", "\n", ":download:`Lancer le notebook sur binder (lent) `" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans ce tutoriel, on apprendra à modéliser un nuage de points." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reprenons l'exemple de la loi d'Ohm (Tutoriel sur le tracé d'un graphique $1^{ère}$ partie) et modélisons la courbe obtenue." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Affichons la courbe obtenue avant modélisation :" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# tableaux numpy impératifs pour réaliser le calcul (vectorisé) de \n", "# la tension modélisée Umodel lors de la modélisation ultérieure\n", "\n", "I=np.array([0,25e-3,50e-3,75e-3,100e-3,125e-3]) \n", "U=np.array([0,1.8,3.3,5.2,6.8,8.5]) \n", "\n", "fig = plt.figure(figsize=(12,10))\n", "plt.plot(I,U,'r+',label='U=f(I)')\n", "plt.legend()\n", "plt.xlabel(\"intensité I (A)\")\n", "plt.ylabel(\"tension U (V)\")\n", "plt.grid()\n", "plt.title(\"Caractéristique Intensité-Tension d’un dipôle ohmique\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Première partie : Modélisation à l'aide de la fonction polyfit de la bibliothèque numpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si le nuage de points est modélisable par une fonction polynomiale, on peut utiliser la fonction **np.polyfit( x , y , deg)** de la bibliothèque numpy as np.\n", "\n", "Le paramètre deg correspond au degré du polynôme.\n", "\n", "La fonction polyfit retourne un tableau numpy à une dimension de coefficients p qui minimisent l'erreur à l'aide de la méthode des moindres carrés dans l'ordre : deg , deg-1 ,… 0 \n", "\n", "$P(x) = p[0] \\times x^{deg} + p[1] \\times x^{deg-1}... + p[deg]$\n", "\n", "D'autres paramètres existent. Pour plus d'informations :\n", "\n", "https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.polyfit.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans le cadre du programme de physique-chimie au lycée, on a besoin au plus de polynômes du second degré.\n", "\n", "- Pour un polynôme de degré 2 : $P(x)=p[0]\\times x^2+p[1]\\times x+p[2]$\n", "- Pour un polynôme de degré 1 : $P(x)=p[0]\\times x+p[1]$\n", "- Pour un polynôme de degré 0 : $P(x)=p[0]$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Modélisons maintenant la courbe obtenue par une droite (polynôme de degré 1) et affichons les coefficients :" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "67.88571428571427 0.023809523809522625\n" ] } ], "source": [ "coeff=np.polyfit(I, U,1)\n", "print(coeff[0],coeff[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Avec une décimale :" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "67.9 0.0\n" ] } ], "source": [ "print ('{0:.1f}'.format(coeff[0]),'{0:.1f}'.format(coeff[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Créons l'équation de la droite modélisée grâce à ces coefficients et affichons cette équation ainsi que son tableau de valeurs :" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "U=67.9 xI+0.0\n", "[0.02380952 1.72095238 3.41809524 5.1152381 6.81238095 8.50952381]\n" ] } ], "source": [ "Umodel = coeff[0]*I+coeff[1] \n", "print('U={0:.1f}'.format(coeff[0]),'xI+{0:.1f}'.format(coeff[1]))\n", "print(Umodel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Affichons maintenant les points expérimentaux et la droite modélisée sur le même graphique :\n", "\n", "**NOTE CODAGE : 'r+' à la ligne 2 permet d'afficher un + rouge pour chaque point expérimental alors que 'b-' à la ligne 3 permet de relier les points modélisés par des segments de droite bleus.**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,10))\n", "plt.plot(U,I,'r+',label='U=f(I)')\n", "plt.plot(Umodel,I,'b-',label='modèle linéaire')\n", "plt.legend()\n", "plt.xlabel(\"intensité I (A)\")\n", "plt.ylabel(\"tension U (V)\")\n", "plt.grid()\n", "plt.title(\"Caractéristique Intensité-Tension d’un dipôle ohmique\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Voici le programme dans sa totalité afin d'y voir un peu plus clair !**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "U=67.9 xI+0.0\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "I=np.array([0,25e-3,50e-3,75e-3,100e-3,125e-3]) \n", "U=np.array([0,1.8,3.3,5.2,6.8,8.5])\n", "\n", "coeff=np.polyfit(I, U,1)\n", "Umodel = coeff[0]*I+coeff[1]\n", "print('U={0:.1f}'.format(coeff[0]),'xI+{0:.1f}'.format(coeff[1]))\n", "\n", "fig = plt.figure(figsize=(12,10))\n", "plt.plot(U,I,'r+',label='U=f(I)')\n", "plt.plot(Umodel,I,'b-',label='modèle linéaire')\n", "plt.legend()\n", "plt.xlabel(\"intensité I (A)\")\n", "plt.ylabel(\"tension U (V)\")\n", "plt.grid()\n", "plt.title(\"Caractéristique Intensité-Tension d’un dipôle ohmique\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deuxième partie : Modélisation à l'aide de la fonction linregress du module stats de la bibliothèque scipy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afin de réaliser une régression linéaire, il est aussi possible d'utiliser la fonction **linregress** issue du module stats de la bibliothèque scipy :\n", "\n", "**slope, intercept, r_value, p_value, std_error = stats.linregress(x,y)**.\n", "\n", "Cette fonction est un plus ardue d'utilisation pour des débutants mais permet d'obtenir la valeur du coefficient de corrélation. \n", "Elle retourne cinq variables dont seules les 3 premières sont utiles en spécialité physique :\n", "- slope : le coefficient directeur (ou pente)\n", "- intercept : l'ordonnée à l'origine\n", "- r_value : le coefficient de correlation\n", "- p_value : \"valeur de p bilatérale pour un test d'hypothèse dont l'hypothèse nulle est que la pente est nulle\" : cette valeur sera d'autant plus grande que la probabilité d'avoir une pente nulle est importante. Par contre, si la probabilité d'avoir une pente nulle est nulle, alors cette valeur sera nulle elle-aussi.\n", "- std_error : \"Erreur standard d'estimation\" : plus cette valeur est faible et plus l'estimation de la pente est précise.\n", "\n", "Pour plus d'informations : \n", "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Modélisons maintenant la courbe obtenue par une droite et affichons les variables de sortie qui nous intéresse :" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pente : 67.88571428571429\n", "ordonnée à l'origine : 0.023809523809523725\n", "coefficient de corrélation r : 0.999720478354276\n" ] } ], "source": [ "from scipy import stats\n", "slope, intercept, r_value, p_value, std_error = stats.linregress(I,U)\n", "print ('pente :',slope)\n", "print (\"ordonnée à l'origine :\", intercept)\n", "print ('coefficient de corrélation r :',r_value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Créons l'équation de la droite modélisée et affichons cette équation ainsi que son tableau de valeurs :" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "U=67.9 xI+0.0\n", "[0.02380952 1.72095238 3.41809524 5.1152381 6.81238095 8.50952381]\n" ] } ], "source": [ "Umodel = slope*I+intercept\n", "print ('U={0:.1f}'.format(slope),'xI+{0:.1f}'.format(intercept))\n", "print(Umodel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Affichons maintenant les points expérimentaux et la droite modélisée sur le même graphique :" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,10))\n", "plt.plot(U,I,'r+',label='U=f(I)')\n", "plt.plot(Umodel,I,'b-',label='modèle linéaire')\n", "plt.legend()\n", "plt.xlabel(\"intensité I (A)\")\n", "plt.ylabel(\"tension U (V)\")\n", "plt.grid()\n", "plt.title(\"Caractéristique Intensité-Tension d’un dipôle ohmique\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Voici le programme dans sa totalité afin d'y voir un peu plus clair !**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "U=67.9 xI+0.0\n", "coefficient de corrélation r : 0.999720478354276\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "I=np.array([0,25e-3,50e-3,75e-3,100e-3,125e-3]) \n", "U=np.array([0,1.8,3.3,5.2,6.8,8.5])\n", "\n", "slope, intercept, r_value, p_value, std_error = stats.linregress(I,U)\n", "Umodel = slope*I+intercept\n", "print ('U={0:.1f}'.format(slope),'xI+{0:.1f}'.format(intercept))\n", "print ('coefficient de corrélation r :',r_value)\n", "\n", "\n", "fig = plt.figure(figsize=(12,10))\n", "plt.plot(U,I,'r+',label='U=f(I)')\n", "plt.plot(Umodel,I,'b-',label='modèle linéaire')\n", "plt.legend()\n", "plt.xlabel(\"intensité I (A)\")\n", "plt.ylabel(\"tension U (V)\")\n", "plt.grid()\n", "plt.title(\"Caractéristique Intensité-Tension d’un dipôle ohmique\")\n", "plt.show()" ] } ], "metadata": { "celltoolbar": "Format de la Cellule Texte Brut", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 }